Mr. Wu Jun Profile

Wu Jun

at Institute of Software Application Technology, Guangzhou

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Proceedings Article | 6 May 2022 Paper

A landmark building detection and recognition based on improved Faster R-RDN algorithm

Wu Jun, Kai Yan, ZiBo Huang, HaiYan Tan, XiaoFang Tu, ChengJun Zhu

Proceedings Volume 12176, 121760C (2022) https://doi.org/10.1117/12.2636502

KEYWORDS: Detection and tracking algorithms, Target detection, Convolutional neural networks, Target recognition, Neural networks, Image processing

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This paper proposes an improved Faster R-DRN (Dense Residual Network, DRN) algorithm, which is based on Faster R-CNN, using densely connected residual network DRNet to replace VGG network. This algorithm is suitable for special scenes of building recognition. It has a residual network and a deep convolution residual network structure, which can efficiently perform image detection, classification and recognition. This design optimizes the problem of algorithm overfitting due to the increase of network depth. In this paper, a comprehensive sample data set for various landmark buildings is established, and samples with different weather, different lighting, and different angles are taken to effectively improve the resistance of the training model. Combined with the optimization of the network structure and the training of targeted data sets, the final feature block diagram generated by DRNet not only does not lose the lowlevel edge texture information, but also reuses the low-level feature block diagrams in the deep convolutional network to make the fused feature block Richer feature information effectively improves the model's recognition rate for photos taken in complex environments. The experimental results show that the accuracy of this method for predicting landmark buildings can reach 82.0% of mAP, and the recognition performance of images taken in complex environments is excellent.

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